#Red Wine Exploratory Data Analysis
By Siye Yu
Today, I will explore the redwine dataset to find out factors which make great quality wine. Everyone may has his or her unique taste, whether its sweetness or bitterness. Let’s first take a look at the dataset data summary to get an better idea before we start.
## 'data.frame': 1599 obs. of 13 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...## X fixed.acidity volatile.acidity citric.acid
## Min. : 1.0 Min. : 4.60 Min. :0.1200 Min. :0.000
## 1st Qu.: 400.5 1st Qu.: 7.10 1st Qu.:0.3900 1st Qu.:0.090
## Median : 800.0 Median : 7.90 Median :0.5200 Median :0.260
## Mean : 800.0 Mean : 8.32 Mean :0.5278 Mean :0.271
## 3rd Qu.:1199.5 3rd Qu.: 9.20 3rd Qu.:0.6400 3rd Qu.:0.420
## Max. :1599.0 Max. :15.90 Max. :1.5800 Max. :1.000
## residual.sugar chlorides free.sulfur.dioxide
## Min. : 0.900 Min. :0.01200 Min. : 1.00
## 1st Qu.: 1.900 1st Qu.:0.07000 1st Qu.: 7.00
## Median : 2.200 Median :0.07900 Median :14.00
## Mean : 2.539 Mean :0.08747 Mean :15.87
## 3rd Qu.: 2.600 3rd Qu.:0.09000 3rd Qu.:21.00
## Max. :15.500 Max. :0.61100 Max. :72.00
## total.sulfur.dioxide density pH sulphates
## Min. : 6.00 Min. :0.9901 Min. :2.740 Min. :0.3300
## 1st Qu.: 22.00 1st Qu.:0.9956 1st Qu.:3.210 1st Qu.:0.5500
## Median : 38.00 Median :0.9968 Median :3.310 Median :0.6200
## Mean : 46.47 Mean :0.9967 Mean :3.311 Mean :0.6581
## 3rd Qu.: 62.00 3rd Qu.:0.9978 3rd Qu.:3.400 3rd Qu.:0.7300
## Max. :289.00 Max. :1.0037 Max. :4.010 Max. :2.0000
## alcohol quality
## Min. : 8.40 Min. :3.000
## 1st Qu.: 9.50 1st Qu.:5.000
## Median :10.20 Median :6.000
## Mean :10.42 Mean :5.636
## 3rd Qu.:11.10 3rd Qu.:6.000
## Max. :14.90 Max. :8.000## X fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 1 7.4 0.70 0.00 1.9 0.076
## 2 2 7.8 0.88 0.00 2.6 0.098
## 3 3 7.8 0.76 0.04 2.3 0.092
## 4 4 11.2 0.28 0.56 1.9 0.075
## 5 5 7.4 0.70 0.00 1.9 0.076
## 6 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5## [1] "X" "fixed.acidity" "volatile.acidity"
## [4] "citric.acid" "residual.sugar" "chlorides"
## [7] "free.sulfur.dioxide" "total.sulfur.dioxide" "density"
## [10] "pH" "sulphates" "alcohol"
## [13] "quality"## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.636 6.000 8.000Univariate Plots Section
Plot shows outlier for two columns, free.sulfur.dioxide and total.sulfur.dioxide
To get a better picture, I’ll create two boxplots for the columns.
Distribution of quality histogram, which shows a normal distrubtin.
Histogram of count for chlorides, citric acid, residual sugar and alcohol variable to show distrubution
Univariate Analysis
What is the structure of your dataset?
it has 1599 obs. of 13 varaiables
What is/are the main feature(s) of interest in your dataset?
whether critic.acid(freshness), sugar(sweatness), chlorides(saltyness) has any impact on the quality of wine.
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
the alochol percentage may help me to answer few question since it has the medium strength of correlation on both density and quality.
Did you create any new variables from existing variables in the dataset?
No new variable was created
Of the features you investigated, were there any unusual distributions?
Did you perform any operations on the data to tidy, adjust, or change the form
of the data? If so, why did you do this?
No, the data is already clean and easy to plot. It doesn’t appear to be useful when analysing the data.
Bivariate Plots Section
## redwine$quality: 3
## [1] 9.955
## --------------------------------------------------------
## redwine$quality: 4
## [1] 10.26509
## --------------------------------------------------------
## redwine$quality: 5
## [1] 9.899706
## --------------------------------------------------------
## redwine$quality: 6
## [1] 10.62952
## --------------------------------------------------------
## redwine$quality: 7
## [1] 11.46591
## --------------------------------------------------------
## redwine$quality: 8
## [1] 12.09444## redwine$quality: 3
## [1] 0.171
## --------------------------------------------------------
## redwine$quality: 4
## [1] 0.1741509
## --------------------------------------------------------
## redwine$quality: 5
## [1] 0.2436858
## --------------------------------------------------------
## redwine$quality: 6
## [1] 0.2738245
## --------------------------------------------------------
## redwine$quality: 7
## [1] 0.3751759
## --------------------------------------------------------
## redwine$quality: 8
## [1] 0.3911111## redwine$quality: 3
## [1] 2.635
## --------------------------------------------------------
## redwine$quality: 4
## [1] 2.69434
## --------------------------------------------------------
## redwine$quality: 5
## [1] 2.528855
## --------------------------------------------------------
## redwine$quality: 6
## [1] 2.477194
## --------------------------------------------------------
## redwine$quality: 7
## [1] 2.720603
## --------------------------------------------------------
## redwine$quality: 8
## [1] 2.577778## redwine$quality: 3
## [1] 0.1225
## --------------------------------------------------------
## redwine$quality: 4
## [1] 0.09067925
## --------------------------------------------------------
## redwine$quality: 5
## [1] 0.09273568
## --------------------------------------------------------
## redwine$quality: 6
## [1] 0.08495611
## --------------------------------------------------------
## redwine$quality: 7
## [1] 0.07658794
## --------------------------------------------------------
## redwine$quality: 8
## [1] 0.06844444Looks like the lower chlorides, the better quality, we will investigate further to make sure the finding
Next let’s look at some of the supporting variables
First take a look at the fixed acidity, as the graph shows, there is almost no correlation between quality and fixed acidity
Graph shows volatile acidity has a negative correlation with quality.
Citric acid has a somewhat postitive relationship with quality, as citric acid increases, the quality also increase
Now let’s take a look at the sweetness factor(residual sugar), there is almost no correlation between residual sugar and quality.
What about chlorides, the graph shows a very weak negative correlation with quality
Free sulfur dioxide doesn’t have much correlation with quality, as the graph shows, it’s almost random without much effect on quality
Total sulfur dioxide has almost the same correlation as free sulfur dioxide with quality, can not infer any impact on quality
As the graph shows, density has a relative weak negative correlation with quality
Last but not the least, alcohol shows somewhat promising positive correlation, which I’ll invetigate further in the next section
Based on the graph, there does not appear to be a strong relationship between quality and total/free.sulfur.dioxide. SO2 is not evident in most of the wines, regardless of quality.
Let’s take a look at the overall correlation between variables. 
## X fixed.acidity volatile.acidity
## X 1.000000000 -0.26848392 -0.008815099
## fixed.acidity -0.268483920 1.00000000 -0.256130895
## volatile.acidity -0.008815099 -0.25613089 1.000000000
## citric.acid -0.153551355 0.67170343 -0.552495685
## residual.sugar -0.031260835 0.11477672 0.001917882
## chlorides -0.119868519 0.09370519 0.061297772
## free.sulfur.dioxide 0.090479643 -0.15379419 -0.010503827
## total.sulfur.dioxide -0.117849669 -0.11318144 0.076470005
## density -0.368372087 0.66804729 0.022026232
## pH 0.136005328 -0.68297819 0.234937294
## sulphates -0.125306999 0.18300566 -0.260986685
## alcohol 0.245122841 -0.06166827 -0.202288027
## quality 0.066452608 0.12405165 -0.390557780
## citric.acid residual.sugar chlorides
## X -0.15355136 -0.031260835 -0.119868519
## fixed.acidity 0.67170343 0.114776724 0.093705186
## volatile.acidity -0.55249568 0.001917882 0.061297772
## citric.acid 1.00000000 0.143577162 0.203822914
## residual.sugar 0.14357716 1.000000000 0.055609535
## chlorides 0.20382291 0.055609535 1.000000000
## free.sulfur.dioxide -0.06097813 0.187048995 0.005562147
## total.sulfur.dioxide 0.03553302 0.203027882 0.047400468
## density 0.36494718 0.355283371 0.200632327
## pH -0.54190414 -0.085652422 -0.265026131
## sulphates 0.31277004 0.005527121 0.371260481
## alcohol 0.10990325 0.042075437 -0.221140545
## quality 0.22637251 0.013731637 -0.128906560
## free.sulfur.dioxide total.sulfur.dioxide density
## X 0.090479643 -0.11784967 -0.36837209
## fixed.acidity -0.153794193 -0.11318144 0.66804729
## volatile.acidity -0.010503827 0.07647000 0.02202623
## citric.acid -0.060978129 0.03553302 0.36494718
## residual.sugar 0.187048995 0.20302788 0.35528337
## chlorides 0.005562147 0.04740047 0.20063233
## free.sulfur.dioxide 1.000000000 0.66766645 -0.02194583
## total.sulfur.dioxide 0.667666450 1.00000000 0.07126948
## density -0.021945831 0.07126948 1.00000000
## pH 0.070377499 -0.06649456 -0.34169933
## sulphates 0.051657572 0.04294684 0.14850641
## alcohol -0.069408354 -0.20565394 -0.49617977
## quality -0.050656057 -0.18510029 -0.17491923
## pH sulphates alcohol quality
## X 0.13600533 -0.125306999 0.24512284 0.06645261
## fixed.acidity -0.68297819 0.183005664 -0.06166827 0.12405165
## volatile.acidity 0.23493729 -0.260986685 -0.20228803 -0.39055778
## citric.acid -0.54190414 0.312770044 0.10990325 0.22637251
## residual.sugar -0.08565242 0.005527121 0.04207544 0.01373164
## chlorides -0.26502613 0.371260481 -0.22114054 -0.12890656
## free.sulfur.dioxide 0.07037750 0.051657572 -0.06940835 -0.05065606
## total.sulfur.dioxide -0.06649456 0.042946836 -0.20565394 -0.18510029
## density -0.34169933 0.148506412 -0.49617977 -0.17491923
## pH 1.00000000 -0.196647602 0.20563251 -0.05773139
## sulphates -0.19664760 1.000000000 0.09359475 0.25139708
## alcohol 0.20563251 0.093594750 1.00000000 0.47616632
## quality -0.05773139 0.251397079 0.47616632 1.00000000Correlation Coefficient: based on the correlation coefficient, only few variables has large strength of association ( > 0.5 or < -0.5). citric.acid - fixed.acidity density - fixed.acidity pH - fixed.acidity critic.acid - volatile.acidity pH - ctic.acid total.sulfur.dioxide - free.sulfur.dioxide Additionally, none of the variable has strong corrleation with quality, only “alcohol” comes closest at 0.47.
Bivariate Analysis
Summary: based on the correlation coefficient and graph, there are weak correlation between the citric.acid(freshness), residual.sugar(sweatness), chlorides(saltyness) and quality. Though the preliminary grouping results showed a possible correlation.
Talk about some of the relationships you observed in this part of the
investigation. How did the feature(s) of interest vary with other features in
the dataset?
All three variables(citric.acid, residual.sugar, cholrides) has weak or none correlation with other variables, except critic.acid and pH.
Did you observe any interesting relationships between the other features
(not the main feature(s) of interest)?
There is strong correlation between fixed.acidity with density, citric.acid, and pH. This means fixed.acidity is a strong component of red wine. Although, this is kind of expected due to the acid nature. But in the same vein, citric.acid and volatile.acidity does not have strong correlation with those same variables. Based on these findings, I suspect the rating can be subjective, instead of objective. Which we will investigate further.
What was the strongest relationship you found?
Between fixed.acidity with density, citric.acid, and pH.
Multivariate Plots Section
Critric acid does not seems to have a strong relationship with residual suger in term of quality 
Also is the relationship between chlorides and residual suger
So is between citric acid and chlorides
Now with quality, let’s take a look at some of the variables. Hope it will clue us in. First stop: chlorides. 
But the graph is not promising, the correlation is weak and negative.
Also, sweetness does not seem to be a factor in term of quality
Overall chlorides has a weak negative correlation with quality
These charts confirmed my suspicion on citric acid which does not seem to play a significant role in quality. Now let’s take a look at
Multivariate Analysis
Talk about some of the relationships you observed in this part of the
investigation. Were there features that strengthened each other in terms of
looking at your feature(s) of interest?
The graphs illustrated what I discovered the previous section. There are
weak correlation between the critic.acid, residual.sugar, and cholrides.
Were there any interesting or surprising interactions between features?
Nothing out of ordinary, except some outliers on many graphs.
OPTIONAL: Did you create any models with your dataset? Discuss the
strengths and limitations of your model.
Due to the data’s cleaness, it was fairly easy create graph and not much
difficulty was encontered. This may not be the same when apply to other
datasets. ——
Final Plots and Summary
Plot One
Description One
There is no significant relationship between the chloride and wine quality. Remove this varilable as a key variable responsible for wine’s quality.
Plot Two
Description Two
This graph also shows the lack of relationship between residual sugar and quality of wine. Even though in common sense, the sweetness usually is associated with the quality of wine as personal taste. But the graph does not show correlation between them.
Plot Three
Description Three
Critical acid has strong correlation than the previous two variables in relation of quality. With the data points listed on the garph, it clearly presents the image that the correlation is still not strong enough. Even though people would think that is one of the common criteria for wine quality.
Reflection
Without break down the data, it is easy to get lost in this giant dataset and let conventional wisdom takes over. As I set out at the beginning, the relationship among variables were totally unexpected. As I went through the whole process, it became more and more clear that my expectation was false. And the correlation and graphs proved me wrong. In data analyasis, how to differentiate personal belief and data can be challenge. I need to have an empty mindset to start with. That way, it will be easier to fall into the “trap”.
A further investigation into the dataset can be wether correlation equal causation for this dataset. Even though we find some variables stronger correlation with quality. But was it the correct correlation, or was it by chance.